The present invention relates to the field of computer processing of medical images. More specifically the invention relates methods of detecting low-contrast and narrow width blood vessels in angiographic and retinal images.
Current methods of detecting low-contrast and narrow-width vessels, while avoiding false responses near pathologies and other non-vascular structures, are lacking and insufficient.
Reliable vessel extraction is a prerequisite for subsequent image analysis and processing, especially in the case of retinal and cardiac images. For certain medical diagnostic tasks, it is necessary to measure accurately the width of vessels and their abnormal branching. For example, tracing a vessel with varying width along the way may reveal the signs of stenosis, i.e., narrowing of the vessels. Grading of stenosis is important to diagnose the severity of vascular disease and subsequently determine the treatment therapy. See, for example, North American Symptomatic Carotid Endarterectomy Trial (NASCET), Steering Committee, North American symptomatic carotid endarterectomy trial, Stroke 22 (1991) 711{720}; and European Carotid Surgery Trialists Collaborative Group, Randomised Trial Of Endarterectomy For Recently Symptomatic Carotid Stenosis: Final Results of the MRC European Carotid Surgery (ECST), Lancet 351 (1999) 1379{87}.
Planning and performing neurosurgical procedures require an exact insight into the complete blood vessels tree structure. This can be achieved by tracing its variability in terms of width as well as direction.
The techniques published in the research literature in the field of vessel extraction have been based on matched filters, adaptive thresholds, mathematical morphology, and Hessian measures. Recent literature has been dominated by Hessian-based methods due to its successful use in characterizing elongated structure of vessels. One advantage of Hessian base methods is that vessels in a large range of diameters can be captured due to multi-scale analysis. In Hessian based methods, an input image is first convolved with the derivatives of a Gaussian at multiple scales and then the Hessian matrix is analyzed at each pixel in the resulting image to determine the local shape of the structures at that pixel. The ratio between the minimum and the maximum Hessian eigenvalues is small for line-like structures but is high for blob-like ones.
However, processing vessel images which are noisy and suffer from non-uniform illumination have the same limitations which Hessian-based filters suffer from in finding narrow and low-contrast vessels. The reason is that such processing uses the same Hessian eigenvalues to pre-select the vessel-candidate pixels at which the filter is applied. In order to reduce the noise sensitivity of the Hessian-based methods, has been proposed to use it within the framework of the directional filter bank. See P. T. H. Truc, M. A. U. Khan, Y K. Lee, S. Lee, T S. Kim, “Vessel Enhancement Filter Using Directional Filter Bank”, Computer Vision and Image Understanding 113 (2009) 101-112. Specifically, the input image is first decomposed by a decimation-free directional filter bank (DDFB) into a set of directional images, each of which contains line-like features in a narrow directional range. The directional decomposition has two advantages. One advantage is that noise in each directional image will be significantly reduced compared to that in the input image due to its random directional nature. The other advantage is that because a directional image contains vessels with similar directions, the Hessian eigenvalue calculation in it is facilitated. Then, distinct appropriate enhancement filters are applied to enhance vessels in the respective directional images. Finally, the enhanced directional images are re-combined to generate the output image with enhanced vessels and suppressed noise. However, since eigen values determination is largely effected by non-uniform illumination patterns, combining directional images for maximizing the vessel extraction is a challenging task. See F. Zana and J.-C. Klein, “Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation,” IEEE Trans. Image Processing, vol. 10, no. 7, pp. 1010-1019, 2001.